Particularly within the past decade or so, substantial effort has been made to provide techniques for disaggregating or decomposing total building or other facility energy consumption usage or load into the actual principal end-use in the facility in order to obtain an understanding of the energy uses in the facility, from which improvements in energy consumption can be suggested, evaluated and hopefully ultimately implemented. Not only is the consumer interested in the economy and energy saving, but also there is often no sales tax levied on manufacturing use of power as distinguished from other uses, making segregation of uses of value; and the utilities themselves obtain incentives in public utility commission rate setting for energy conservation, as well.
Among these techniques are those using utility electric bills, building audit data, end-use metering and computer simulation as sources of information as to the facility's energy performance. One such example is described in an article, entitled "An Algorithm to Disaggregate Combined Whole-Building Hourly Electric Load into End-Uses" by H. Akbari et al, of Laurence Berkeley Laboratory, appearing at pages 10.13 through 10.26 of a U.S. Department of Energy publication identified as under Contract No. DE-ACU3-76SF00098,1989, and in which an extensive bibliography of prior techniques is presented. Limitations in prior approaches to analysis through the disaggregation of monthly electric utility billing data into end-use or time-of-use information are pointed out in said article, including the general requirement for complete detailed building information, insufficient detail on time of use and building operations within such monthly data, aggregation of daytime with nighttime use as well as weekday with weekend use, and other limitations. Such has led Akbari et al, in said article, and others skilled in this art to conclude that only end-use load profile information, as through end-use submetering on the real operation of a building and the end uses within it, can provide the appropriate disaggregation information.
In accordance with the present invention, however, new techniques have been developed for more effective use and analysis of electric utility billing data in consort with other information that obviate such and other limitations and, indeed, through reiterative processing of data recollation and re-analysis, can operate with minimal data (as well as, of course, with complete data for a facility), and ultimately provide a greater level of certainty about what is happening in the building concerning the consumption of energy.
The electrical work week (EWW) (hours use of peak demand per week) is extremely significant for developing the rules (the expert system) regarding how to disaggregate an electric bill. Such rules for disaggregating a bill are developed by auditing hundreds of buildings, though each building does not have to have a complete audit. Various subsets of the building population are constructed with the primary determinants being building functions (SIC code) square footage, demand per square foot, consumption per square foot and EWW. Statistical population norms are developed for each identifiable subset, thereby allowing the disaggregation model to `Learn` as it grows, as more fully later described.
Underlying the significant improvement of the present invention, indeed, is the apparently heretofore missed significance that such hours of peak demand per week can provide an excellent basis for estimating hours of use for the facility--such being readily obtainable by providing both kilowatt (KW) and kilowatt hours (Kwh) information, dividing Kwh by KW, and normalizing the result to a seven-day period.
Indeed, the process underlying the present invention proves erroneous the current belief in the art that monthly data provides insufficient detail and that detailed building information is required; providing a modeling system which is designed to develop accuracy from monthly data through recursive processing of the information fed into it and developed by it. Through obtaining hours of use by dividing the monthly Kwh by peak demand KW (i.e., the ratio Kwh/KW=Hours), and disaggregating peak demand to obtain an effective connected load by end-use at peak demand (and with check figures to validate assumptions), and then testing the predicted assumption over an entire 12-month period, the present invention, at the point that sufficient data has been gathered from a statistically sufficient portion of the population, overcomes the need for detailed information to obtain reasonable results from only the billing data and square footage.
The elimination of such need for detailed information at the point that sufficient data has been gathered from a statistically significant portion of the population is particularly highly important for energy conservation potential analysis and for energy savings verification.
To illustrate the expert system segment of the disaggregation model, the following summary example may be considered.
A retail store, as an illustration, has a highly consistent pattern of use (store hours). Multiple linear regression of bills has proven this assertion. To achieve a lighting system retrofit, a detailed inventory of the fixtures is performed. Existing connected load is based upon manufacturer's data, and energy conservation potential is calculated from the differential between existing and proposed connected load and an estimate of hours of operation. After such retrofit is performed, the actual Kwh savings is calculated by comparing a base year consumption, which is adjusted for weather variation to the current year consumption, as later more fully discussed. The variation in peak demand is also calculated by subtracting current year demand from base year.
The resulting answer may now be compared with estimates. The change in effective connected load at peak demand (ECLPD) which was recorded by the utility billing allows fine tuning of the original estimate. The actual Kwh savings allows refinement of the estimate of hours of operation. With such performed a few dozen times, enough significant data can be gathered to predict what is happening in a building for which only the bills, its use and the square footage is available. This also provides the ability accurately to estimate the energy conservation potential.
By inputting the standard hours of occupancy for the building for every day in the billing period, therefore, and collating this fact to the billing period and deriving the total hours of operation from the bill, problems of the aggregating of daytime and nighttime use as well as weekday with weekend use are admirably overcome. Strong statistical correlations are achieved with the process of the invention by optimizing the lighting/power and process hours of operation of effective connected load at peak demand.
In a preferred modeling system, the power end-use contains the process load, as opposed to a combined lighting and power end-use with separate process end-use. Such is accomplished as hereinafter explained by the ability of the model to handle operating schedules.
This is effected by defining sub-classes of each end-use. Lighting, for example, is comprised of offices, hallways, bathrooms, closets, etc. according to building SIC code and other statistics developed in the expert system.
A critical component of the novel process of the invention, indeed, is the discovered capability to expand the structure of the data collection from a minimalist viewpoint to a very precise representation of what is transpiring in the building, with the end-use categories disaggregated into their individual components and devices, and/or aggregated into sets of data which correspond to the patterns of information needs of the facility operators. If, moreover, the billing information provides peak and off-peak consumption data, then hours of use can be cross-correlated to the time periods as defined by the particular utility.
The process of the invention, indeed, simultaneously disaggregates the utility bill while it develops an optimal set of statistics which accurately describe the monthly variations in billing data. To effect this analysis, a regression equation is employed to establish a relationship with weather that is both linear (to describe the hours of use components of the bill) and polynomial (because the relation of consumption to weather is a curve)--OAT+OAT.sup.2 (outside average temperature).
Some buildings, moreover, may require more than a second order polynomial regression of OAT (weather). The process, therefore, defines a methodology which selects the best curve. Quite simply, it regresses weekdays, weekends, (accounting for annual variation in occupancy) and multiple orders of OAT, and selects the best correlation and F-test combination.
The process further defines certain normative statistics which allow the user to know whether the bill is actually being properly disaggregated; and, as before-pointed out, it operates extremely well with minimal data for a facility (as well as with complete data, if available). Through developing or predicting an appropriate load shape for the effective connected load at peak demand (ECLPD) for lighting, power (plug load and office equipment) and process (production related) and end-use categories in the facility, such as a predicted square wave representing weekly power consumption, the regression analysis can verify and optimize the assumptions as to the load shape according to what patterns of consumption and demand actually happen at the facility; in short, verifying and modifying original predictions or assumptions as to such patterns and hours of use, within a predetermined margin, to develop a regression equation with high correlation and minimal monthly residuals.
One of the elegant uses of this "square wave" (the nature of the visual display conveys better information if it is displayed as stacked area charts) is to incorporate limited real time monitoring with the estimating process. Basically, a recording meter may be used for 1 week or longer to produce an actual operating hour/demand curve to compare with estimates. The incorporation of such real time data significantly improves the reliability of the disaggregation for any particular building.
As previously mentioned, if more than one time period is described by the utility bill (such as peak, shoulder and off-peak hours of consumption) then the analysis is performed for each time period.
An expanded hours of operation data input screen used together with the previously mentioned "square wave" display greatly simplifies the required entry. In conjunction with a utility bill which covers multiple time periods, this method allows for an automated fine tuning of assumptions.